Auto Text Summarization in Natural Language Processing: Review

Automated text summarization is a pivotal task in natural language processing (NLP), tasked with condensing expansive texts into concise, informative abstracts. This review study provides an in-depth survey of the recent techniques in NLP and challenges. This study categorizes summarization methods into extractive, abstractive, and hybrid techniques, Extractive methods efficiently select key sentences but may miss nuanced content, while abstractive techniques, including advanced models like BERT and GPT-3, generate new content but struggle with coherence and source fidelity. Hybrid methods attempt to integrate these approaches but face complexity issues. The study evaluates common metrics like ROUGE, METEOR, and CIDEr, highlighting their limitations in accurately assessing summary quality. It addresses challenges such as handling idiomatic language, computational demands, and summarizing large-scale data. The main objective is to highlight these issues and propose a research direction focused on overcoming these limitations to enhance the efficiency, accuracy, and scalability of text summarization.